1. Field of the Invention
The present invention generally relates to natural language question and answer systems, and more particularly to an automated method of recommending a specific business process in response to a natural language question.
2. Description of the Related Art
As interactions between users and computer systems become more complex, it becomes increasingly important to provide a more intuitive interface for a user to issue commands and queries to a computer system. As part of this effort, many systems employ some form of natural language processing. Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation allowing computers to respond in a manner familiar to a user. For example, a non-technical person may input a natural language question to a computer system, and the system intelligence can provide a natural language answer which the user can hopefully understand. Examples of an advanced computer systems that use natural language processing include virtual assistants, Internet search engines, and cognitive systems such as the Watson™ cognitive technology marketed by International Business Machines Corp.
Text analysis is known in the art pertaining to NLP and typically uses a text annotator program to search text documents (corpora) and analyze them relative to a defined set of tags. The text annotator can generate linguistic annotations within the document to tag concepts and entities that might be buried in the text. A cognitive system can then use a set of linguistic, statistical and machine-learning techniques to analyze the annotated text, and extract key business information such as person, location, organization, and particular objects (e.g., vehicles), or identify positive and negative sentiment. The Watson system relies on hypothesis generation and evaluation to rapidly parse relevant evidence and evaluate potential responses from disparate data. End users can pose certain questions in a natural language for which the system responds with a procedural answer (with associated evidence and confidence). For example, an end user might ask any of the following natural language questions:
When does my phone contract end?
What is the procedure to return my defective device?
How can I put a temporary suspend on my salary ACH?
What is the process to file an insurance claim?
How do I raise an inquiry on my billing statement?
For each of these questions, a natural language question and answer (NLQA) system can be trained to come back with a generalized answer, typically pointing to other sources which can provide procedural responses that tell the end user what to do textually.